The Progression of Interstitial Fibrosis and Tubular Atrophy at 6 Months Is an Independent Predictor of Poor Graft Outcomes in Kidney Transplant Recipients
Bibliographic record
Abstract
Interstitial fibrosis and tubular atrophy (IFTA) found on 1-y surveillance biopsies has been associated with poor graft outcomes. However, its progression over time and relationship to outcomes are less well defined. Methods: We studied implantation and 6-mo surveillance biopsies and examined the association between the progression of IFTA (ΔIFTA) and a composite of censored graft loss or doubling of serum creatinine in 248 adult kidney recipients. Results: The percentage of patients with ΔIFTA of 1 or ≥2 was 35% and 22%, respectively. Positive ΔIFTA was a risk factor for the composite endpoint (hazard ratio, 1.36; 95% confidence interval, 1.03-1.79). This estimate was robust to adjustment for recipient and donor baseline characteristics, baseline IFTA, tacrolimus levels, and rejection status. ΔIFTA was associated with decreased estimated glomerular filtration rate at 3 and 5 y. IFTA+i was a predictor in the cohort; however, IFTA progression was not limited to those with a mononuclear cell interstitial inflammation (Banff "i") score above zero. Notably, donor age was a predictor of IFTA at 6 mo, but not of ΔIFTA, whereas rejection, donor diabetes, and recipient smoking status were. Conclusions: Progression of IFTA at 6 mo can predict outcomes. ΔIFTA was not related to donor age but may be linked to other risk factors influencing decision-making for donor versus recipient selection.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".